Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes’ sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model’s output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15–20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.

The purpose of the present study was to determine the relationship between reaction time and deception type and investigate the effect of deception type on reaction time during smash in Badminton. Sex Badminton players are high level athletes in the world participated in the last Olympic championship "London 2012", (mass 69.17±6.31 kg, length 178.00±0.06 cm. Reaction time is defined as the period of time that elapses between offensive player stroke the shuttle and opponent player move to shuttle direction. Smashes were analyzed of the last six matches in Olympic championship London 2012, two matches in Quarter-finals, two matches of Semi-finals, Bronze Medal Match and Gold Medal Match. Dartfish v.7 software motion analysis used to analysis 230 smashes and for the statistical analysis of the data the IBM SPSS Statistics 21 was used. The complex deception is more difficult types of deception for the opponents as the player use more than a tool during the striking and then the degree of difficulty followed by arm deception, while the performance of the smashes without deception gives a greater opportunity for an opponent to anticipate the strike and then stopped, and this means that the increase the degree of difficulty of deception increased the time of reaction necessary to repel the strike, thereby increasing the opportunity to make the point, therefore must specify a part in the content of the training programs for the smashes combined with different types of deception.

The purpose of this pilot study was to evaluate the relationship between the kinematic variables of the right hand and left leg with ball velocity during jump-throwing phases in handball for better-informed training. We investigated ball velocity and the key kinematic variables of jump throwing during different throwing phases in three strides. Ten right-handed male handball professional players who had competed in the Egyptian Handball Super League participated in this study. Jump throwing performance was divided into three phases (cocking, acceleration and follow-through), which included eight events during the throwing. Five trials were captured for each player, and a 3D analysis was performed on the best trial. Results indicated that the velocity of the throwing hand was the most important variable during jump throwing, which was correlated with ball velocity during the three phases of performance in four events: Initial contact (IC) (r = 0.66*), initial flight (IF) (r = 63*), maximum height of the throwing hand (Max-HH) (r = 0.78*) and ground contact (GC) (r = 0.83*). In addition, the initial flight was the most important event in which players need to be using the best angles during performance, particularly the shoulder angle.